Functional, Physical and Sensory Properties of Pulse Ingredients Incorporated into Orange and Apple Juice Beverages
Bibliographic record
Abstract
The objective of this study was to explore the use of pulse ingredients in the development of orange juice and apple juice supplemented beverages. Commercially available pulse ingredients including pea protein (PP), chickpea flour (CPF), lentil flour (LF) and pea fibre (PF) were selected and characterized with respect to specific functional properties (water holding capacity, fat absorption capacity, protein solubility, emulsifying and foaming properties). Apple juice was supplemented with 1-4% pulse ingredients, whereas a supplementation level of 1-2% was used for orange juice. The physical and sensory properties of the supplemented beverages were measured after production and during 3 weeks of refrigerated storage. Sensory attributes for both orange and apple juice supplemented with 1% and 2% pulse ingredients were similar to their respective controls (with and without pectin added). In terms of turbidity, supplementation increased the turbidity of apple juice and orange juice beverages at all levels, in comparison with control and pectin-added control samples. Supplemented samples, showed less satisfactory results in terms of cloud stability and color especially for orange juice beverages in comparison with their respective controls. Overall, while there are some hurdles to be overcome, the results suggest that when used at the 1-2% levels, PP, PF, CPF and LF could serve as potential value-added ingredients for beverage supplementation based on their physical and sensory properties. Further studies are, however, required in this promising area to improve the stability of the final production especially during storage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".